- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | YANG bin, HU Jinming, ZHANG Qilin, WANG Congjun. Location Information Perception of Onsite Construction Crew Based on Person Re-identification[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20230125
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To obtain location information of onsite construction crew continuously with the consideration of dynamical changing, occluding, and high appearance similarity in construction scenes, a computer vision-based location information perception method for onsite construction crew was proposed. Firstly, a deep learning-based object detection method was utilized to percept targets preliminarily. Then, a data association method based on person re-identification was used, where ID assignment was completed by matching the deep learning-based feature. A distance metric method based on re-ranking was utilized to optimize the similarity measurement results, and the matching result was processed by using a buffering mechanism and a dynamical feature updating mechanism, so as to mitigate mismatch due to difficulties in construction scenes. 2D coordinates and movement information corresponding to ID were obtained using perspective transformation of images to provide basic data for productivity analysis. Finally, standard test videos were created from images collected at different construction stages to test the proposed method. The test results show that in different scenes, the average F1 score of ID (IDF1) and multiple object tracking accuracy (MOTA) of the algorithm are 85.4% and 75.4%, respectively. The proposed re-ranking method and post-processing mechanism for matching effectively improve the tracking accuracy. Compared with the algorithm after removing these optimization mechanisms, the average improvement of IDF1 and MOTA is 52.8% and 3.8%, respectively.
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